• Substantive research interests
  • Broader question: Emergence of AfD as party and parliamentary presence - what are the effects on party competition and parliamentarism?
  • Descriptive (preliminary) question: What are the prevalent framings in speeches given by AfD parliamentarians?
  • Contagion hypothesis (diffusion): (Speakers of) other parliamentary groups may take over framings offered by AfD speakers.
  • cp. DFG-Projekt “The populist challenge in parliament” (2019-2021, cooperation with Christian Strecker, Marcel Lewandowsky, Jochen Müller)
• Methodological interests
  • Validity and intersubjectivity of data-driven, “distant reading” approaches (in the eHumanities)
  • ML/AI: Annotation to gain training data for statistical learning => gold standard annotation, scores to evaluate trained models
  • Social sciences: Traditions of coding and annotating text data: Quantitative/qualitative content analysis

• Combining distant and close reading is an unfulfilled promise: Software often inhibits combining both perspectives. How to implement workflows for coding and annotating textual data? The polmineR R package (and a few brothers and sisters) are presented as potential solution.

• Special focus: Interactive graph annotation as an approach to generate intersubjectively shared interpretations/understandings of discourse patterns.

• Schedule:
  • Theory is code
  • The MigParl corpus
  • AfD Keywords
  • Graph annotation
  • Conclusions

• Why R?
  • the most common programming language in the social sciences
  • comprehensive availability of statistical methods
  • great visualisation capabilites
  • usability: RStudio as IDE
  • reproducible research: Rmarkdown notebooks
• Why the Corpus Workbench (CWB)?
  • a classic toolset for corpus analysis
  • indexing and compression of corpora => performance
  • powerful and versatile syntax of the Corpus Query Processor (CQP)
  • permissive license (GPL)
• NoSQL / Lucene / Elasticsearch are potential alternatives - but not for now

The “triple”:

• polmineR: basic vocabulary for corpus analysis

• RcppCWB: wrapper for the Corpus Workbench (using C++/Rcpp, follow-up on rcqp-package)

• cwbtools: tools to create and manage CWB indexed corpora

Beyond the triple:

• GermaParl: documents and disseminates GermaParl corpus
• frappp: framework for parsing plenary protocols
• annolite: light-weight fulltext display and annotation tool
• topicanalysis: workflows with quantitative/qualitative elements for topic models
• gradget: graph annotation widget

• Getting started with polmineR is easy: Assuming that R and RStudio are installed, polmineR can be installed as simple as follows (dependencies such as RcppCWB will be installed automatically).

install.packages("polmineR")

• Get the GermaParl corpus(corpus of plenary debates in the German Bundestag).

drat::addRepo("polmine") # add CRAN-style repository to known repos
install.packages("GermaParl") # the downloaded package includes a small sample dataset
GermaParl::germaparl_download_corpus() # get the full corpus

• That’s it. Ready to go.

library(polmineR)
use("GermaParl") # activate the corpora in the GermaParl package, i.e. GERMAPARL

• the good old workflow to create partitions (i.e. subcorpora)

p <- partition("GERMAPARL", year = 2001)
m <- partition("GERMAPARL", speaker = "Merkel", regex = TRUE)

• the emerging new workflow …

am <- corpus("GERMAPARL") %>% subset(speaker == "Angela Merkel")

m <- corpus("GERMAPARL") %>% subset(grep("Merkel", speaker)) # Petra Merkel!

cdu_csu <- corpus("GERMAPARL") %>%
  subset(party %in% c("CDU", "CSU")) %>%
  subset(role != "presidency")

dt <- dispersion("GERMAPARL", query = "Flüchtlinge", s_attribute = "year")
barplot(height = dt$count, names.arg = dt$year, las = 2, ylab = "Häufigkeit")

q <- '[pos = "NN"] "mit" "Migrationshintergrund"'
corpus("GERMAPARL") %>% kwic(query = q, cqp = TRUE, left = 10, right = 10)

kwic("GERMAPARL", query = "Islam", positivelist = c(good, bad)) %>%
  highlight(lightgreen = good, orange = bad) %>%
  tooltips(setNames(SentiWS[["word"]], SentiWS[["weight"]])) %>%
  knit_print()

partition("GERMAPARL", date = "2009-11-10", speaker = "Merkel", regex = T) %>%
  html(height = "250px") %>%
  highlight(list(yellow = c("Bundestag", "Regierung")))

p <- partition("BE", date = "2005-04-28", speaker = "Körting", regex = TRUE, verbose = FALSE)
sc <- corpus("BE") %>%  subset(date == "2005-04-28") %>% subset(grepl("Körting", speaker))
ek <- as.speeches(p, s_attribute_name = "speaker", verbose = FALSE)[[4]]
h <- get_highlight_list(BE_lda, partition_obj = ek, no_token = 150)
lapply(h, function(x) x[1:8])

html(p, height = "350px") %>% highlight(highlight = h)

• The cooccurrences()-method can be applied to subcorpora / partitions, and corpora.

cooccurrences("GERMAPARL", query = 'Islam', left = 10, right = 10)

Starting with polmineR v0.7.9.11, the package includes a method to efficiently calculate all cooccurrences in a corpus.

m <- partition("GERMAPARL", year = 2008, speaker = "Angela Merkel", interjection = F)
drop <- terms(m, p_attribute = "word") %>% noise() %>% unlist()
Cooccurrences(m, p_attribute = "word", left = 5L, right = 5L, stoplist = drop) %>% 
  decode() %>% # 
  ll() %>%
  subset(ll >= 10.83) %>%
  subset(ab_count >= 5) -> coocs

• widget1
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• widget3